At first you will hear a description of many functions and abilities of intelligent beings (us).

If you keep insisting: “No, I don’t need a description of it, I want a definition”; the best answer you get is … (“I’ll keep you in suspense”, but if you want to know the answer immediately, just scroll down the page).

Definitions represent the skeleton of a science (any science).

If a research field does not have clear and operating definitions for all the fundamental terms it uses, it is not yet a true science, at the best, it is a science in making.

"Intelligence ... is an ability ... to solve new problems" // W.V. bingham. Aptitudes and aptitude testing. Harper & Brothers, New York, 19371. from my point of view that is implied and obvious.2. a definition of something, including Intelligence, should be concise, sufficient on its own, without the need for additional explanation of a possible interpretation.3. the host of intelligence does NOT have to use it wide, the definition should allow to observe (measure, assess) one individual and make a conclusion if the host has or doesn’t have Intelligence (e.g. Turing tests).4. Intelligence should not depend on a specific field of action; the property/ability/feature called “Intelligence” should be “field-independent”, which makes it “field-universal”, meaning, if it works in one field, it will work any any/every field. The ability to create solutions to problems which have never been solved before is exactly of that type. Giving a good definition is very important, and not always easy. Take, for example, a famous tale about Plato and Diogenes, which says that “when Plato gave the tongue-in-cheekdefinition of man as "featherless bipeds," Diogenes plucked a chicken and brought it into Plato's Academy, saying, "Behold! I've brought you a man"” (https://en.wikipedia.org/wiki/Diogenes).

Nowadays, stories about new AI achievements are everywhere. But what is AI? What is a definition of it?

The article “Artificial Intelligence” in Encyclopedia Britannica is composed of about 8000 words. It can be divided in three major parts.

The first part (the shortest one) simply says that artificial intelligence is like human intelligence but artificially manufactured.

The second part is a shorter version of the article about human Intelligence.

The third part describes various technical approaches to constricting AI.

The clearest and actual definition of AI is provided in the first part, i.e. AI is artificially manufactured system which can do what HI (human intelligence) can.

I think this is the best approach to define any artificial object which has a biological counterpart ("an artificial arm" for example, is "an artificially made arm"), because it fits the Occam's razor criterion.

That leads us to search for a definition of intelligence, in general (HI represents a biological realization of it).

The article “Human Intelligence” in Encyclopedia Britannica is composed of about 9000 words. It describes various approach to understanding what intelligence is, its aspects, elements, properties, manifestations.

But this article does not give a clear definition of intelligence.

Here are the first two sentences from the article, quote:

“Human intelligence, mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.

Much of the excitement among investigators in the field of intelligence derives from their attempts to determine exactly what intelligence is.”

The article provides a short description for many various attempts, but does not offer one description of what intelligence is, which would dominate the field.

Without having a formal definition of AI, searching for AI would be like “go there don’t know where, and find that don’t know what” (this is what Tsar said to Andrei the Solder, according to a famous Russian tale).

So, every researcher in the field of AI development has some definition of AI (because, clearly, they know "that, what they want to find").

But without having one commonly accepted definition of intelligence (or anything else, for that matter) every researcher who is trying to construct AI (or anything else, for that matter) can base the attempts on the description which fits the best his or her own views.

Of course, the majority of actors in the field base their actions on something they all have in common (that common part defines the field).

And that common part which defines the field of AI is patterns.

Everyone in the field accepts, as the basis for all R&D activities, the fact that intelligence does not exist without pattern recognition. But as the result, many researches in the field of AI shrank intelligence to pattern recognition, i.e. they simple made intelligence and pattern recognition equal: intelligence is pattern recognition.

BTW: it happens every time when the process of the development of a definition of an object is based on listing the set of attributes the object has or doesn't have ("men are featherless bipeds").

Of course, deep inside they all know that this approach is wrong (or at least insufficient), but without having a definition of intelligence, that is the best they can do.

And we all know that intelligence (at least HI) is more than just an ability to recognize or produce patterns, because animals also can recognize patters. Even more, animal world provides many examples of complicated pattern development (e.g. bees, termites, beavers, spiders).

As an expert in HI, I have been searching for a simple, clear, workable, operational definition of intelligence. In 2017 this search has finally come to an end.

This is my definition of intelligence:

Intelligence is the property of a system; the mission, the reason for its existence, and the core ability of intelligence iscreating solutions to problems which have never been solved before (by that system).

All other aspects of intelligence (heavily discussed in literature, and artfully presented in Encyclopedia Britannica) play their roles, and take their places as devices, components, abilities, organs, functions required for intelligence to exist, perform, and achieve its goals, fulfill its mission - creating, again and again, a solution to a problem which has never been solve before.

When the host of intelligence (e.g. a human person) creates a solution to a problem the host has never solved before (or has no memory of that) but that problem has been solved in the past by other host(s), the intelligence plays only local role – for that host only.

But when the host of intelligence (e.g. a human person) creates a solution to a problem NO host has ever solved before, the result has a global value – for the whole assembly of hosts (e.g. human society).

BTW: teaching creativity (a.k.a. critical thinking, creative thinking, lateral thinking, inventiveness) is “simply” teaching students how to create solutions to problems they have never solved before, i.e. teaching students how to be intelligent (what I have been successfully doing for many years: www.GoMars.xyz/evvv.html).

Naturally, my definition of AI is based on a subset of definition, for example, on a specific view on what a problem is, what does it mean “to solve a problem”, and much more (that is why I have been intensely publishing all my blogs).

In this piece, I only also want to present the difference between a problem and a task:

1. When someone needs to achieve a goal, and knows what actions to perform in order to achieve it, it is not a problem it is a task.

2. When someone needs to achieve a goal, and does NOT know what actions to perform in order to achieve it, that IS a problem.

The definitions above represent the simplest description of “a task” and “a problem”, but already can be used as the means for differentiating intelligent actions from routine actions.

There is one more question, the answer to which affects the whole discussion: “What is a scientific definition of “a scientific definition”?”.

I like to ask my students a short version of it: “What is a definition of “a definition”?”, and it always makes them think hard, and generates a discussion.

Everyone is welcome to join this discussion (BTW: this discussion is essential, crucial for the final choice of the actual definition).

So, what is the meaning of AI – as a symbol (abbreviation)?

Well, first and foremost it is the ultimate goal of the R&D in the field of AI development.

But currently, it is a brilliant marketing instrument, helping to promote the R&D in the field. The actual abbreviation should be APRS for an Artificial Pattern Recognition System, but AI of course is much cooler!

At first you will hear a description of many functions and abilities of intelligent beings (us).

If you keep insisting: “No, I don’t need a description of it, I want a definition”; the best answer you get is:

“AI is an artificially manufactured pattern recognition system which can expand/advance/increase/broaden the scope of its own functions without human interference”.

This definition accurately describes human intelligence.

However, this definition also accurately describes animal behavior.

So, if you are fine with being on the same level with animals, you can keep using that definition.

Otherwise, I suggest switching to mine.

BTW: there is a simple way out of this conundrum, which is introducing two definitions:

1. General Intelligence (GI) is a pattern recognition system which can expand/advance/increase/broaden the scope of its own functions without interference from other intelligent systems.

2. Human Intelligence (HI) is the property of a system; the mission, the reason for its existence, and the core ability of intelligence is creating solutions to problems which have never been solved before (by that system).

In that case, the current meaning of AI becomes the equivalent of AGI (artificial GI), which includes HI and another AI (animal I).

The ultimate AI, which does not yet exist, but heavily described as almost here, is AHI (artificial HI).

The part which has no name because it goes
after the EPILOGUE which is by the definition is the last part of a written
pieceThe distinct,
unique, crucial, necessary and sufficient attribute, feature, property,
expression of intelligence is (wait for it) – a DOUBT.Creating a solution to a problem which have
never been solved before inevitably
leads to some uncertainties, to the situations when there is no (not exists) purely
logical reasoning leading to the answer, to the goal, to the expected result. In
this situation an intelligent subject always KNOWS that this is the time when
the only possible action is to “go with the gut”, “to flip a coin”. The result – “do this” – is based on
fluctuations in the neural network of networks called a brain. This is what
no current so called “AI” can do. Current “AI” has no doubts. It makes the decision
(“this is this face”, “this is this word”, “this is this …”) based on the training
it had. The better its training was, the less mistakes it makes (e.g. looking
at a banana and seeing a face). But the current “AI” never doubts its choices; currently an HI (human intelligence)
needs to interfere to check “AI”s decisions (if only HI was always smarter than
“AI”: https://teachologyforall.blogspot.com/2018/02/Facebook.html).
Until an
artificial brain learns how to process fluctuations in its network, artificial intelligence
will not be in actual intelligence but merely an efficient recognition device.

Appendix I: a conversation with a professional

Recently I was informed about a 2007 paper, containing the survey of various definitions of Intelligence (https://arxiv.org/abs/0712.3329). The list is very impressive. I found two relatively similar to mine.

I would say my definitions includes this one, but make a more specific statement, which makes it more operational.

"Intelligence ... is an ability ... to achieve goals" (belongs to the authors of the paper).

I would argue, that essentially that translates into my definition, with the goal "to achieve the solution constructed to a problem which have never been solved before.

I had an email exchange, in which I was pointed at the importance of "being able to solve problems in various environments (solving wide range of problems, achieving wide range of goals)".

I responded that I would not consider an additional description, namely: "wide range” - as an important part of the definition of intelligence, due to the following reasons:

"Achieving a goal" (in any practical or theoretical field) when you KNOW how to achieve it is very much different from a situation when you DON'T know how to achieve it and have to develop/design the solution (procedure, protocol, device); that ability is the central core of HI, or I in general (please, refer again to my differentiation between "a problem" and "a task").

Speaking about the definition of AI, my view is that, no matter what definition of Intelligence is used, Machine I, or AI, is just Intelligence developed artificially. There is no need for a special definition (like artificial arm). It may make sense for internal use between AI developers, but for general public, practitioners, educators A just literally means "Artificial". Although, that would require a discussion about the meaning of word "definition", including what is its purpose.

In conclusion, I would like to make a point that if a commonly accepted definition of Intelligence existed, it would be presented in the corresponded article in Encyclopedia Britannica. Since that is not a case, the question is still open, and the discussion remains vital.

Appendix II: another conversation with another professional (with a slight editing)

Dear Valentin,

I'm not so so keen on definitions. You know the old challenge, can you define a game?

Best.

Dear Dr. …,

thank you very much for your note.

I follow the General Theory of Human Activity:

· science is one of human practices;

· as such it evolves, has phases and stages; and levels;

· the direction of evolution of science does not depend on the actual field;

· there is a stage when people in the field do not have commonly accepted definitions;

· there is a stage (the higher one) when people in the field have developed commonly accepted definitions;

· this transition is inevitable and unavoidable;

· and, of course, in every science, there are terms which cannot be defined (the root terms), but it does not mean nothing can be defined, on the contrary, everything which can be defined needs to be defined;

· and if something (mass, charge, game, intelligence) has been defined, it does not mean that definition will not evolve in the future;

· one of the goals of a scientific methodology is to separate the categories in the field as definable and non-definable.

2. the participant or participants can choose to participate or not in that activity without damaging consequences

3. the participant or participants chose to follow specific and the same rules

4. there is a specific rule or rules (a criterion) which describe when the game is finished and what is the result

5. (the most important distinction between a "game" and "not a game" - like "this is not a game anymore"); a game does not significantly alter the everyday life of the participants, i.e. after the game is finished the participants can return to the pre-game state; the status might be changed, e.g. "a winner", "a loser", but the change in the status does not have a strong effect on the post-game life. Of course, the meaning of words "significant", "strong" is not defined very well. That is why, in my view, this is not a full definition, conditions 1 - 5 are necessary, but not sufficient, and condition 5 is "fuzzy". But this "pre-definition" would be applicable to the majority of games.

I would be happy to have coffee with you some time, if you have such an opportunity.

Sincerely,

Valentin

BTW: so far no coffee

Appendix III: on the general structure of a problem solving process

The general structure of a problem solving process, or PSP (i.e. the process required to solve a problem; i.e. the process required to create a solution to a problem), does NOT depend on the problem; in particular, it does not depend on the field to which the problem belongs. That means that (1) one needs to learn how to design the PSP in one field - and the BEST field to do that would be physics (here is why: (A) a text, https://teachologyforall.blogspot.com/2016/12/onphysics.html; (B) slides, http://www.gomars.xyz/1717.html, slides 59-61 point at a relationship between cyber thinking and thinking);

then (2) one needs to learn how to transfer that skill to solve problems in another field (does not matter which one),

and (3) after that the one will be able to transfer that problem solving skill (PSS) to ANY field.

This is the the description of the fundamental basis (not presented in literature) for teaching with establishing reliable transfer of knowledge.